| Summary: | 碩士 === 國立臺灣大學 === 電機工程學研究所 === 103 === Dynamic Adaptive Streaming over HTTP (DASH) has become an emerging
application nowadays. Video rate adaptation is a key to determine the
video quality of HTTP-based media streaming. Recent works have proposed
several algorithms that allow a DASH client to adapt its video encoding rate to
network dynamics. While network conditions are typically affected by many
different factors, these algorithms however usually consider only a few representative
information, e.g., predicted available bandwidth or fullness of its
playback buffer. In addition, the error in bandwidth estimation could significantly
degrade their performance. Therefore, this paper presents Machine-
Learning-based Adaptive Streaming over HTTP (MLASH), an elastic framework
that exploits a wide range of useful network-related features to train
a rate classification model. The distinct properties of MLASH are that its
machine-learning-based framework can be incorporated with any existing adaptation
algorithm and utilize big data characteristics to improve prediction accuracy.
We show via trace-based simulations that machine-learning-based
adaptation can achieve a better performance than traditional adaptation algorithms
in terms of their target quality of experience (QoE) metrics.
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